Microsoft Technology Licensing, LLC

Rajasi Saha

Ashish Consul

Harvey Rook

Shengquan Yan

John Carlton Platt

Saliha Azzam

Jayaram N M Nanduri

Julian Alexander Zbogar-Smith

Harish Jayanti

Danilo Landucci Benzatti

Andrew John Sullivan

Galen Michael Elias

Xiaoguang Qi

Christopher J Mallery

Chad A Zanonie

Michael Y Moon

Methods, systems, apparatuses, and computer program products are described for providing content recommendations to a requestor. In implementations, user event records associated with media items and content types may be stored, for instance, in an event catalog. A set of user scores may be generated based on a user-content score model and the user event records, where each generated user score represents an affinity between a user and a corresponding content type of the plurality of content types. A set of title scores may also be generated for a media item based on a title-content score model, where each generated title score represents an affinity between the media item and a corresponding content type. In response to a request, the sets of scores may be combined to generate a set of content recommendations to provide to the requestor.

Multi-content recommendation system combining user model, item model and real time signals

Architecture that employs machine-based learning to automatically categorize data on a per-user basis. Auto-tagging reduces the burden on infoworkers by creating a machine learning model to learn from user tagging behavior or preferences. Once this information is obtained, a trained model for this specific user is used to assign tags to incoming data, such as emails. The architecture finds particular applicability to compliance and message retention policies that otherwise would mandate extra work for the infoworker. The architecture learns the tagging behavior of a user and uses this learned behavior to automatically tag data based on the user's prior tagging habits. A regression algorithm is employed to process the training data according to an n-dimensional framework for prediction and application of the tag(s) to the incoming messages.

Machine-based learning for automatically categorizing data on per-user basis

Techniques to manage retention policy tags are described. A system may comprise a tagging component operative to tag a message with an expiration tag to form an expiration tagged message, the expiration tag having expiration information used to expire the expiration tagged message in accordance with a retention policy. The system may further comprise a presentation component communicatively coupled to the tagging component, the presentation component operative to present a view of the expiration tagged message with an expiration field having the expiration information for the expiration tagged message.

Techniques to manage retention policy tags

Determining indicative features may be provided. First, a first set of features may be determined using a document frequency process. Then a second set of features may be determined using a boosting process. Using the boosting process may comprise using an approximation for a one-dimensional optimization. The approximation may include an upper bound. Next, the first set of features and the second set of features may be combined into a combined set of features. The combined set of features may comprise a union of the first set of features and the second set of features. At least one document may then be classified based on the combined set of features.

Boosting to determine indicative features from a training set

This disclosure is directed to automatically moving message objects, such as email message objects, from online source mailbox databases to online archive mailbox databases. As described herein, each email message in a source mailbox database is directly or indirectly associated with a retention policy tag. The retention policy tag associated with a message object in a source mailbox database specifies a retention period. The message object remains in source mailbox database until the retention period expires for the message object. When the retention period for the message object expires, the message object is automatically moved to an archive mailbox database.

Online archiving of message objects

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Sammamish, WA, United States of America

Rajasi Saha